• Title/Summary/Keyword: 공간 공분산 행렬

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Underdetermined blind source separation using normalized spatial covariance matrix and multichannel nonnegative matrix factorization (멀티채널 비음수 행렬분해와 정규화된 공간 공분산 행렬을 이용한 미결정 블라인드 소스 분리)

  • Oh, Son-Mook;Kim, Jung-Han
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.2
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    • pp.120-130
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    • 2020
  • This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.

Blind Signal Subspace Channel Estimation technique for DS-CDMA DMB downlink (DS-CDMA DMB 하향링크에서의 블라인드 신호공간 채널추정 기법)

  • Yang, Wan-Chul;Lee, Byung-Seu
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.9A
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    • pp.1039-1047
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    • 2004
  • In this paper, we propose a new channel estimation technique for long code DS-CDMA DMB down link system which estimate the channel response based on the signal space vector only, unlike the most conventional sub-space method relying on the orthogonal property of noise space vectors to the signal space vector. Because of this property of the proposed method, very optimum covariance matrix in its dimension can be used in subspace analysis channel estimation technique otherwise it is likely too large to be implemented practically.

Wideband adaptive beamforming method using subarrays in acoustic vector sensor linear array (부배열을 이용한 음향벡터센서 선배열의 광대역 적응빔형성기법)

  • Kim, Jeong-Soo;Kim, Chang-Jin;Lee, Young-Ju
    • The Journal of the Acoustical Society of Korea
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    • v.35 no.5
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    • pp.395-402
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    • 2016
  • In this paper, a wideband adaptive beamforming approach for an acoustic vector sensor linear array is presented. It is a very important issue to estimate the stable covariance matrix for adaptive beamforming. In the conventional wideband adaptive beamforming based on coherent signal-subspace (CSS) processing, the error of bearing estimates is resulted from the focusing matrix estimation and the large number of data snapshot is necessary. To alleviate the estimation error and snapshot deficiency in estimating covariance matrix, the steered covariance matrix method in the pressure sensor is extended to the vector sensor array, and the subarray technique is incorporated. By this technique, more accurate azimuth estimates and a stable covariance matrix can be obtained with a small number of data snapshot. Through simulation, the azimuth estimation performance of the proposed beamforming method and a wideband adaptive beamforming based on CSS processing are assessed.

Fast DOA Estimation Algorithm using Pseudo Covariance Matrix (근사 공분산 행렬을 이용한 빠른 입사각 추정 알고리듬)

  • 김정태;문성훈;한동석;조명제;김정구
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.40 no.1
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    • pp.15-23
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    • 2003
  • This paper proposes a fast direction of arrival (DOA) estimation algorithm that can rapidly estimate incidence angles of incoming signals using a pseudo covariance matrix. The conventional subspace DOA estimation methods such as MUSIC (multiple signal classification) algorithms need many sample signals to acquire covariance matrix of input signals. Thus, it is difficult to estimate the DOAs of signals because they cannot perform DOA estimation during receiving sample signals. Also if the D0As of signals are changing rapidly, conventional algorithms cannot estimate incidence angles of signals exactly. The proposed algorithm obtains bearing response and directional spectrum after acquiring pseudo covariance matrix of each snapshot. The incidence angles can be exactly estimated by using the bearing response and directional spectrum. The proposed DOA estimation algorithm uses only concurrent snapshot so as to obtain covariance matrix. Compared to conventional DOA estimation methods. The proposed algorithm has an advantage that can estimate DOA of signal rapidly.

A Trimmed Spatial Median Estimator Using Bootstrap Method (붓스트랩을 활용한 최적 절사공간중위수 추정량)

  • Lee, Dong-Hee;Jung, Byoung-Cheol
    • The Korean Journal of Applied Statistics
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    • v.23 no.2
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    • pp.375-382
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    • 2010
  • In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.

Covariance Matrix Estimation with Small STAP Data through Conversion into Spatial Frequency-Doppler Plane (적은 STAP 데이터의 공간주파수-도플러 평면 변환을 이용한 공분산행렬 추정)

  • Hoon-Gee Yang
    • Journal of IKEEE
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    • v.27 no.1
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    • pp.38-44
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    • 2023
  • Performance of a STAP(space-time adaptive processing) algorithm highly depends on how closely the estimated covariance matrix(CM) resembles the actual CM by the interference in CUT(cell under test). A STAP has 2 dimensional data structure determined by the number of array elements and the number of transmitting pulses and both numbers are generally not small. Thus, to meet the degree of freedom(DOF) of the CM, a huge amount of training data is required. This paper presents an algorithm to generate virtual training data from small received data, via converting them into the data in spatial frequency-Doppler plane. We theoretically derive where the clutter exist in the plane and present the procedure to implement the proposed algorithm. Finally, with the simulated scenario of small received data, we show the proposed algorithm can improve STAP performance.

A Subspace-based Blind Interference Cancellation for the DS/CDMA System (직접수열 코드분할 다중접속 시스템의 부공간 기반 미상 간섭 제거 기법)

  • 윤연우;김형명
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.26 no.11B
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    • pp.1510-1521
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    • 2001
  • In this paper a subspace-based blind interference cancellation is proposed and its performance is analyzed. Then the blind adaptive implementation is devolped using the improved natural power method which is the signal subspace tracking algorithm. The theoretical analysis shows that when the exact covariance matrix is kown the performance of the proposed detector is the same as that of the decorrelating detector. And when the covariance matrix is estimated the asymptotic results are examined. The results of computer simulation demonstrate that the proposed detector outperforms the previous blind adaptive RLS MOE detector.

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A Study on Spatial Smoothing Technique for Angle of Arrival Estimation of Coherent Incoming Waves (코히어런트 입사파의 도래방향 추정을 위한 공간평균법의 개선에 관한 연구)

  • Jeong Jung-Sik
    • Journal of Navigation and Port Research
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    • v.29 no.5 s.101
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    • pp.403-408
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    • 2005
  • The techniques of estimating angle of arrival(AOA) have played a key role for enhancement of wireless communications using array antennas. Among those techniques, the superresolution algorithms, such as MUSIC and ESPRIT, calculate the covariance matrix of the array output vectors which are observed at the array antennas, and then by using eigen-decomposition of the covariance matrix, they estimate AOAs of the received signals with high accuracy. However, superresolution algorithms based eigenvalue decomposition fails to estimate AOAs under multipath environments. Under multipath environments, it is difficult to estimate AOAs of the received signals due to coherency and high-correlation. To resolve coherent signals, the covariance matrix is calculated by using the conventional spatial smoothing technique, and then the techniques based on eigen-descomposition is applied. The result of the conventional spatial smoothing technique, however, is obtained at the cost of losing effective spatial aperture. Moreover, the conventional technique ignores any information in the cross-correlations of the array outputs the subarrays. As the result, the performance for AOA estimation is degraded. In this paper, we propose a new spatial smoothing technique, which consider the cross-correlation for subarrays. By computer simulation, the AOA estimation performance of the proposed method is compared with the conventional method and evaluated.

Direction of Arrival Estimation in Colored Noise Using Wavelet Decomposition (웨이브렛 분해를 이용한 유색잡음 환경하의 도래각 추정)

  • Kim, Myoung-Jin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.37 no.6
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    • pp.48-59
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    • 2000
  • Eigendecomposition based direction-of-arrival(DOA) estimation algorithm such as MUSIC(multiple signal classification) is known to perform well and provide high resolution in white noise environment. However, its performance degrades severely when the noise process is not white. In this paper we consider the DOA estimation problem in a colored noise environment as a problem of extracting periodic signals from noise, and we take the problem to the wavelet domain. Covariance matrix of multiscale components which are obtained by taking wavelet decomposition on the noise has a special structure which can be approximated with a banded sparse matrix. Compared with noise the correlation between multiscale components of narrowband signal decays slowly, hence the covariance matrix does not have a banded structure. Based on this fact we propose a DOA estimation algorithm that transforms the covariance matrix into wavelet domain and removes noise components located in specific bands. Simulations have been carried out to analyze the proposed algorithm in colored noise processes with various correlation properties.

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Design of Downlink Beamformer for High-quality.High-speed Wireless Multimedia Services (고품질.고속 무선 멀티미디어 서비스를 위한 송신 빔 형성기 설계)

  • 이용주;양승용;김기만
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.5 no.3
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    • pp.459-464
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    • 2001
  • We propose a transmit beamforming algerian for array antenna in FDD (Frequency Division Duplex) environments. The proposed method estimates the directions and spectra of the users, and constructs the spatial covariance matrix of the interferences at the downlink frequency. The weights are computed by that covariance matrix and desired user's direction vector Simulations are performed under Rayleigh fading environments. The proposed method don't need the data feedback, has the enhanced performance in BER (Bit Error Rate).

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